1 code implementation • 24 Jan 2023 • Samraj Moorjani, Adit Krishnan, Hari Sundaram, Ewa Maslowska, Aravind Sankar
While existing approaches demonstrate textual style transfer with large volumes of parallel or non-parallel data, we argue that grounding style on audience-independent external factors is innately limiting for two reasons.
1 code implementation • 28 Feb 2022 • Aravind Sankar
With the rapid proliferation of such online services, learning data-driven user behavior models is indispensable to enable personalized user experiences.
1 code implementation • 11 Sep 2020 • Aravind Sankar, Junting Wang, Adit Krishnan, Hari Sundaram
We present InfoMotif, a new semi-supervised, motif-regularized, learning framework over graphs.
1 code implementation • 5 Jun 2020 • Aravind Sankar, Yanhong Wu, Yuhang Wu, Wei zhang, Hao Yang, Hari Sundaram
We study the problem of making item recommendations to ephemeral groups, which comprise users with limited or no historical activities together.
2 code implementations • 1 Jan 2020 • Aravind Sankar, Xinyang Zhang, Adit Krishnan, Jiawei Han
Recent years have witnessed tremendous interest in understanding and predicting information spread on social media platforms such as Twitter, Facebook, etc.
2 code implementations • 22 Dec 2018 • Aravind Sankar, Yanhong Wu, Liang Gou, Wei zhang, Hao Yang
Learning latent representations of nodes in graphs is an important and ubiquitous task with widespread applications such as link prediction, node classification, and graph visualization.
1 code implementation • 15 Nov 2017 • Aravind Sankar, Xinyang Zhang, Kevin Chen-Chuan Chang
This paper introduces a generalization of Convolutional Neural Networks (CNNs) to graphs with irregular linkage structures, especially heterogeneous graphs with typed nodes and schemas.